Individuals manifest different functional limitations at different times during their older years, and the specific limitations experienced by a given individual change as that person ages. Although functional limitations can occur in many domains, only cognitive functioning is considered here. Information about cognitive functioning will come from two different sources of longitudinal disability information: the Health and Retirement Study (HRS) and the study of Assets and Health Dynamics of the Oldest-Old (AHEAD). The HRS consists of a nationally representative sample of pre-retirement-age persons. The AHEAD consists of persons 70 years and older. This study will pilot the use of Bayesian latent class (BLC) models to investigate cognitive functioning in older adults. Previous work using other data sources to address similar problems has employed a variety of techniques for longitudinal data analysis; however, problems with standard methodologies have prevented adequate characterization of the heterogeneity of disabilities manifested in older adults. BLC models are similar to latent variable or factor analytic (LV/FA) models and directly related to Grade-of-Membership (GoM) models. Like LV/FA models, BLC models seek to describe the data in terms of a small set of latent random variables. Like GoM models, BLC models allow individuals to be flexibly associated with multiple latent classes. In addition, BLC models address changes in actual disability profiles, not changes in summary scores, and are readily estimable and easily interpreted. As part of this pilot study, we will fit BLC models to two waves of data from both the HRS and AHEAD studies, incorporating important covariate measures such as age, sex and other predictors of functional limitation. In addition, we will instigate various means to (a) introduce within- person longitudinal correlation, (b) adjust for missing data, and (c) account for death in follow-up waves. Ultimately, we are interested in describing the relationship between longitudinal patterns in cognitive functioning and other aging outcomes. The BLC models to be employed in this pilot study represent an important next step towards better characterization and understanding of age-related changes in cognitive functioning, which will increase our ability to predict important health and cost outcomes.